Worst-case analysis of heap allocations

Abstract. In object oriented languages, dynamic memory allocation is a fundamental concept. When using such a language in hard real-time systems, it becomes important to bound both the worst-case execution time and the worst-case memory consumption. In this paper, we present an analysis to determine the worst-case heap allocations of tasks. The analysis builds upon techniques that are well established for worst-case execution time analysis. The difference is that the cost function is not the execution time of instructions in clock cycles, but the allocation in bytes. In contrast to worst-case execution time analysis, worst-case heap allocation analysis is not processor dependent. However, the cost function depends on the object layout of the runtime system. The analysis is evaluated with several real-time benchmarks to establish the usefulness of the analysis, and to compare the memory consumption of different object layouts.

Abstract Interpretation of FIFO Replacement

In hard real-time systems, the execution time of programs must be bounded by static timing analysis. For today’s embedded systems featuring caches, static analyses must predict cache hits and misses with high precision to obtain useful bounds. For caches with least-recentlyused (LRU) replacement policy, efficient and precise cache analyses exist. However, for other widely-used policies like first-in first-out (FIFO), current cache analyses are much less precise. This paper discusses challenges in FIFO cache analysis and advances the state of the art. We identify a generic framework for cache analysis that couples may- and must-analyses by means of domain cooperation. Our main contribution is a more precise may-analysis for FIFO. It not only increases the number of predicted misses, but also—due to the domain cooperation—the number of predicted hits. We instantiate the framework with a canonical must-analysis and three different may-analyses, including our new one, and compare the resulting three analyses to the collecting semantics. Our evaluation results characterize the progress achieved by our new may-analysis and reveal room for further improvement